Hardware Trends Impacting Floating-Point Computations In Scientific Applications
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912163621240832 |
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| author | Dongarra, Jack Gunnels, John Bayraktar, Harun Haidar, Azzam Ernst, Dan |
| author_facet | Dongarra, Jack Gunnels, John Bayraktar, Harun Haidar, Azzam Ernst, Dan |
| contents | The evolution of floating-point computation has been shaped by algorithmic advancements, architectural innovations, and the increasing computational demands of modern technologies, such as artificial intelligence (AI) and high-performance computing (HPC). This paper examines the historical progression of floating-point computation in scientific applications and contextualizes recent trends driven by AI, particularly the adoption of reduced-precision floating-point types. The challenges posed by these trends, including the trade-offs between performance, efficiency, and precision, are discussed, as are innovations in mixed-precision computing and emulation algorithms that offer solutions to these challenges. This paper also explores architectural shifts, including the role of specialized and general-purpose hardware, and how these trends will influence future advancements in scientific computing, energy efficiency, and system design. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_12090 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hardware Trends Impacting Floating-Point Computations In Scientific Applications Dongarra, Jack Gunnels, John Bayraktar, Harun Haidar, Azzam Ernst, Dan Numerical Analysis 90C10, 65F99, 65G30, 65G99, G.1.3; G.4; B.2.m; D.3.4 The evolution of floating-point computation has been shaped by algorithmic advancements, architectural innovations, and the increasing computational demands of modern technologies, such as artificial intelligence (AI) and high-performance computing (HPC). This paper examines the historical progression of floating-point computation in scientific applications and contextualizes recent trends driven by AI, particularly the adoption of reduced-precision floating-point types. The challenges posed by these trends, including the trade-offs between performance, efficiency, and precision, are discussed, as are innovations in mixed-precision computing and emulation algorithms that offer solutions to these challenges. This paper also explores architectural shifts, including the role of specialized and general-purpose hardware, and how these trends will influence future advancements in scientific computing, energy efficiency, and system design. |
| title | Hardware Trends Impacting Floating-Point Computations In Scientific Applications |
| topic | Numerical Analysis 90C10, 65F99, 65G30, 65G99, G.1.3; G.4; B.2.m; D.3.4 |
| url | https://arxiv.org/abs/2411.12090 |